



'''simple docstring'''


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roformer.modeling_flax_roformer import (
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						)




class   __UpperCamelCase							(unittest.TestCase      ):







	def __init__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase=13			,					_lowerCAmelCase=7			,					_lowerCAmelCase=True			,					_lowerCAmelCase=True			,					_lowerCAmelCase=True			,					_lowerCAmelCase=True			,					_lowerCAmelCase=99			,					_lowerCAmelCase=32			,					_lowerCAmelCase=5			,					_lowerCAmelCase=4			,					_lowerCAmelCase=37			,					_lowerCAmelCase="gelu"			,					_lowerCAmelCase=0.1			,					_lowerCAmelCase=0.1			,					_lowerCAmelCase=512			,					_lowerCAmelCase=16			,					_lowerCAmelCase=2			,					_lowerCAmelCase=0.02			,					_lowerCAmelCase=4			,					)     ->     Union[str, Any]:
							'''simple docstring'''






							lowercase   =				parent
							lowercase   =				batch_size
							lowercase   =				seq_length
							lowercase   =				is_training
							lowercase   =				use_attention_mask
							lowercase   =				use_token_type_ids
							lowercase   =				use_labels
							lowercase   =				vocab_size
							lowercase   =				hidden_size
							lowercase   =				num_hidden_layers
							lowercase   =				num_attention_heads
							lowercase   =				intermediate_size
							lowercase   =				hidden_act
							lowercase   =				hidden_dropout_prob
							lowercase   =				attention_probs_dropout_prob
							lowercase   =				max_position_embeddings
							lowercase   =				type_vocab_size
							lowercase   =				type_sequence_label_size
							lowercase   =				initializer_range
							lowercase   =				num_choices







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size    )

							lowercase   =				None
							if self.use_attention_mask:
													lowercase   =				random_attention_mask([self.batch_size, self.seq_length]    )

							lowercase   =				None
							if self.use_token_type_ids:
													lowercase   =				ids_tensor([self.batch_size, self.seq_length]			,					self.type_vocab_size    )

							lowercase   =				RoFormerConfig(
							    vocab_size=self.vocab_size			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					max_position_embeddings=self.max_position_embeddings			,					type_vocab_size=self.type_vocab_size			,					is_decoder=_lowerCAmelCase			,					initializer_range=self.initializer_range			,					)

							return config, input_ids, token_type_ids, attention_mask







	def 	_a (     self    )     ->     int:
							'''simple docstring'''






							lowercase   =				self.prepare_config_and_inputs()
							lowercase   =				config_and_inputs
							lowercase   =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
							return config, inputs_dict




@require_flax
class   __UpperCamelCase							(_UpperCAmelCase      , unittest.TestCase      ):
	__A      	=       True

	__A      	=       (
	    (
	        FlaxRoFormerModel,
	        FlaxRoFormerForMaskedLM,
	        FlaxRoFormerForSequenceClassification,
	        FlaxRoFormerForTokenClassification,
	        FlaxRoFormerForMultipleChoice,
	        FlaxRoFormerForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				FlaxRoFormerModelTester(self    )







	@slow
	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							for model_class_name in self.all_model_classes:
													lowercase   =				model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""			,					from_pt=_lowerCAmelCase    )
													lowercase   =				model(np.ones((1, 1)    )    )
													self.assertIsNotNone(_lowerCAmelCase    )




@require_flax
class   __UpperCamelCase							(unittest.TestCase      ):







	@slow
	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							lowercase   =				FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""    )
							lowercase   =				jnp.array([[0, 1, 2, 3, 4, 5]]    )
							lowercase   =				model(_lowerCAmelCase    )[0]

							lowercase   =				5_0000

							lowercase   =				(1, 6, vocab_size)
							self.assertEqual(output.shape			,					_lowerCAmelCase    )

							lowercase   =				jnp.array(
							    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]    )

							self.assertTrue(jnp.allclose(output[:, :3, :3]			,					_lowerCAmelCase			,					atol=1E-4    )    )










"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")